Overview

Brought to you by YData

Dataset statistics

Number of variables32
Number of observations9758
Missing cells49932
Missing cells (%)16.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.5 MiB
Average record size in memory703.3 B

Variable types

DateTime1
Categorical5
Numeric21
Text4
Unsupported1

Alerts

CANCELLED has constant value "0.0" Constant
AIRLINE is highly overall correlated with AIRLINE_CODE and 2 other fieldsHigh correlation
AIRLINE_CODE is highly overall correlated with AIRLINE and 2 other fieldsHigh correlation
AIRLINE_DOT is highly overall correlated with AIRLINE and 2 other fieldsHigh correlation
AIR_TIME is highly overall correlated with CRS_ELAPSED_TIME and 3 other fieldsHigh correlation
ARR_DELAY is highly overall correlated with DEP_DELAY and 1 other fieldsHigh correlation
ARR_TIME is highly overall correlated with CRS_ARR_TIME and 4 other fieldsHigh correlation
CRS_ARR_TIME is highly overall correlated with ARR_TIME and 4 other fieldsHigh correlation
CRS_DEP_TIME is highly overall correlated with ARR_TIME and 4 other fieldsHigh correlation
CRS_ELAPSED_TIME is highly overall correlated with AIR_TIME and 2 other fieldsHigh correlation
DELAY_DUE_CARRIER is highly overall correlated with DIVERTEDHigh correlation
DELAY_DUE_LATE_AIRCRAFT is highly overall correlated with DIVERTEDHigh correlation
DELAY_DUE_NAS is highly overall correlated with DIVERTEDHigh correlation
DELAY_DUE_SECURITY is highly overall correlated with DIVERTEDHigh correlation
DELAY_DUE_WEATHER is highly overall correlated with DIVERTEDHigh correlation
DEP_DELAY is highly overall correlated with ARR_DELAYHigh correlation
DEP_TIME is highly overall correlated with ARR_TIME and 4 other fieldsHigh correlation
DISTANCE is highly overall correlated with AIR_TIME and 2 other fieldsHigh correlation
DIVERTED is highly overall correlated with AIR_TIME and 7 other fieldsHigh correlation
DOT_CODE is highly overall correlated with AIRLINE and 2 other fieldsHigh correlation
ELAPSED_TIME is highly overall correlated with AIR_TIME and 3 other fieldsHigh correlation
WHEELS_OFF is highly overall correlated with ARR_TIME and 4 other fieldsHigh correlation
WHEELS_ON is highly overall correlated with ARR_TIME and 4 other fieldsHigh correlation
DIVERTED is highly imbalanced (98.0%) Imbalance
CANCELLATION_CODE has 9758 (100.0%) missing values Missing
DELAY_DUE_CARRIER has 8021 (82.2%) missing values Missing
DELAY_DUE_WEATHER has 8021 (82.2%) missing values Missing
DELAY_DUE_NAS has 8021 (82.2%) missing values Missing
DELAY_DUE_SECURITY has 8021 (82.2%) missing values Missing
DELAY_DUE_LATE_AIRCRAFT has 8021 (82.2%) missing values Missing
CANCELLATION_CODE is an unsupported type, check if it needs cleaning or further analysis Unsupported
DEP_DELAY has 463 (4.7%) zeros Zeros
ARR_DELAY has 173 (1.8%) zeros Zeros
DELAY_DUE_CARRIER has 762 (7.8%) zeros Zeros
DELAY_DUE_WEATHER has 1640 (16.8%) zeros Zeros
DELAY_DUE_NAS has 894 (9.2%) zeros Zeros
DELAY_DUE_SECURITY has 1724 (17.7%) zeros Zeros
DELAY_DUE_LATE_AIRCRAFT has 840 (8.6%) zeros Zeros

Reproduction

Analysis started2025-07-17 04:02:30.395033
Analysis finished2025-07-17 04:02:51.968823
Duration21.57 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Distinct1683
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Memory size152.5 KiB
Minimum2019-01-01 00:00:00
Maximum2023-08-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-17T06:02:52.008931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:52.077077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

AIRLINE
Categorical

High correlation 

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size729.8 KiB
Southwest Airlines Co.
1901 
Delta Air Lines Inc.
1346 
American Airlines Inc.
1207 
SkyWest Airlines Inc.
1101 
United Air Lines Inc.
826 
Other values (13)
3377 

Length

Max length34
Median length22
Mean length19.581882
Min length9

Characters and Unicode

Total characters191080
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSkyWest Airlines Inc.
2nd rowRepublic Airline
3rd rowSkyWest Airlines Inc.
4th rowUnited Air Lines Inc.
5th rowUnited Air Lines Inc.

Common Values

ValueCountFrequency (%)
Southwest Airlines Co. 1901
19.5%
Delta Air Lines Inc. 1346
13.8%
American Airlines Inc. 1207
12.4%
SkyWest Airlines Inc. 1101
11.3%
United Air Lines Inc. 826
8.5%
Republic Airline 462
 
4.7%
Envoy Air 400
 
4.1%
JetBlue Airways 360
 
3.7%
PSA Airlines Inc. 353
 
3.6%
Endeavor Air Inc. 353
 
3.6%
Other values (8) 1449
14.8%

Length

2025-07-17T06:02:52.142283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
inc 6047
20.1%
airlines 5473
18.2%
air 3463
11.5%
lines 2467
8.2%
southwest 1901
 
6.3%
co 1901
 
6.3%
delta 1346
 
4.5%
american 1207
 
4.0%
skywest 1101
 
3.7%
united 826
 
2.7%
Other values (18) 4349
14.5%

Most occurring characters

ValueCountFrequency (%)
i 21928
11.5%
20323
 
10.6%
n 17813
 
9.3%
e 17030
 
8.9%
r 12190
 
6.4%
s 11928
 
6.2%
A 11814
 
6.2%
l 8775
 
4.6%
t 8186
 
4.3%
. 7948
 
4.2%
Other values (33) 53145
27.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 191080
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 21928
11.5%
20323
 
10.6%
n 17813
 
9.3%
e 17030
 
8.9%
r 12190
 
6.4%
s 11928
 
6.2%
A 11814
 
6.2%
l 8775
 
4.6%
t 8186
 
4.3%
. 7948
 
4.2%
Other values (33) 53145
27.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 191080
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 21928
11.5%
20323
 
10.6%
n 17813
 
9.3%
e 17030
 
8.9%
r 12190
 
6.4%
s 11928
 
6.2%
A 11814
 
6.2%
l 8775
 
4.6%
t 8186
 
4.3%
. 7948
 
4.2%
Other values (33) 53145
27.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 191080
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 21928
11.5%
20323
 
10.6%
n 17813
 
9.3%
e 17030
 
8.9%
r 12190
 
6.4%
s 11928
 
6.2%
A 11814
 
6.2%
l 8775
 
4.6%
t 8186
 
4.3%
. 7948
 
4.2%
Other values (33) 53145
27.8%

AIRLINE_DOT
Categorical

High correlation 

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size767.9 KiB
Southwest Airlines Co.: WN
1901 
Delta Air Lines Inc.: DL
1346 
American Airlines Inc.: AA
1207 
SkyWest Airlines Inc.: OO
1101 
United Air Lines Inc.: UA
826 
Other values (13)
3377 

Length

Max length38
Median length26
Mean length23.581882
Min length13

Characters and Unicode

Total characters230112
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSkyWest Airlines Inc.: OO
2nd rowRepublic Airline: YX
3rd rowSkyWest Airlines Inc.: OO
4th rowUnited Air Lines Inc.: UA
5th rowUnited Air Lines Inc.: UA

Common Values

ValueCountFrequency (%)
Southwest Airlines Co.: WN 1901
19.5%
Delta Air Lines Inc.: DL 1346
13.8%
American Airlines Inc.: AA 1207
12.4%
SkyWest Airlines Inc.: OO 1101
11.3%
United Air Lines Inc.: UA 826
8.5%
Republic Airline: YX 462
 
4.7%
Envoy Air: MQ 400
 
4.1%
JetBlue Airways: B6 360
 
3.7%
PSA Airlines Inc.: OH 353
 
3.6%
Endeavor Air Inc.: 9E 353
 
3.6%
Other values (8) 1449
14.8%

Length

2025-07-17T06:02:52.194227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
inc 6047
15.2%
airlines 5473
13.7%
air 3463
 
8.7%
lines 2467
 
6.2%
southwest 1901
 
4.8%
co 1901
 
4.8%
wn 1901
 
4.8%
delta 1346
 
3.4%
dl 1346
 
3.4%
american 1207
 
3.0%
Other values (36) 12787
32.1%

Most occurring characters

ValueCountFrequency (%)
30081
 
13.1%
i 21928
 
9.5%
n 17813
 
7.7%
e 17030
 
7.4%
A 15479
 
6.7%
r 12190
 
5.3%
s 11928
 
5.2%
: 9758
 
4.2%
l 8775
 
3.8%
t 8186
 
3.6%
Other values (45) 76944
33.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 230112
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
30081
 
13.1%
i 21928
 
9.5%
n 17813
 
7.7%
e 17030
 
7.4%
A 15479
 
6.7%
r 12190
 
5.3%
s 11928
 
5.2%
: 9758
 
4.2%
l 8775
 
3.8%
t 8186
 
3.6%
Other values (45) 76944
33.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 230112
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
30081
 
13.1%
i 21928
 
9.5%
n 17813
 
7.7%
e 17030
 
7.4%
A 15479
 
6.7%
r 12190
 
5.3%
s 11928
 
5.2%
: 9758
 
4.2%
l 8775
 
3.8%
t 8186
 
3.6%
Other values (45) 76944
33.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 230112
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
30081
 
13.1%
i 21928
 
9.5%
n 17813
 
7.7%
e 17030
 
7.4%
A 15479
 
6.7%
r 12190
 
5.3%
s 11928
 
5.2%
: 9758
 
4.2%
l 8775
 
3.8%
t 8186
 
3.6%
Other values (45) 76944
33.4%

AIRLINE_CODE
Categorical

High correlation 

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size562.2 KiB
WN
1901 
DL
1346 
AA
1207 
OO
1101 
UA
826 
Other values (13)
3377 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters19516
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOO
2nd rowYX
3rd rowOO
4th rowUA
5th rowUA

Common Values

ValueCountFrequency (%)
WN 1901
19.5%
DL 1346
13.8%
AA 1207
12.4%
OO 1101
11.3%
UA 826
8.5%
YX 462
 
4.7%
MQ 400
 
4.1%
B6 360
 
3.7%
OH 353
 
3.6%
9E 353
 
3.6%
Other values (8) 1449
14.8%

Length

2025-07-17T06:02:52.241595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wn 1901
19.5%
dl 1346
13.8%
aa 1207
12.4%
oo 1101
11.3%
ua 826
8.5%
yx 462
 
4.7%
mq 400
 
4.1%
b6 360
 
3.7%
oh 353
 
3.6%
9e 353
 
3.6%
Other values (8) 1449
14.8%

Most occurring characters

ValueCountFrequency (%)
A 3665
18.8%
O 2555
13.1%
N 2196
11.3%
W 1901
9.7%
L 1346
 
6.9%
D 1346
 
6.9%
U 826
 
4.2%
Y 668
 
3.4%
9 583
 
3.0%
X 529
 
2.7%
Other values (12) 3901
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19516
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 3665
18.8%
O 2555
13.1%
N 2196
11.3%
W 1901
9.7%
L 1346
 
6.9%
D 1346
 
6.9%
U 826
 
4.2%
Y 668
 
3.4%
9 583
 
3.0%
X 529
 
2.7%
Other values (12) 3901
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19516
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 3665
18.8%
O 2555
13.1%
N 2196
11.3%
W 1901
9.7%
L 1346
 
6.9%
D 1346
 
6.9%
U 826
 
4.2%
Y 668
 
3.4%
9 583
 
3.0%
X 529
 
2.7%
Other values (12) 3901
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19516
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 3665
18.8%
O 2555
13.1%
N 2196
11.3%
W 1901
9.7%
L 1346
 
6.9%
D 1346
 
6.9%
U 826
 
4.2%
Y 668
 
3.4%
9 583
 
3.0%
X 529
 
2.7%
Other values (12) 3901
20.0%

DOT_CODE
Real number (ℝ)

High correlation 

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19972.877
Minimum19393
Maximum20452
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size152.5 KiB
2025-07-17T06:02:52.282755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum19393
5-th percentile19393
Q119790
median19930
Q320368
95-th percentile20436
Maximum20452
Range1059
Interquartile range (IQR)578

Descriptive statistics

Standard deviation377.95251
Coefficient of variation (CV)0.018923288
Kurtosis-1.3183646
Mean19972.877
Median Absolute Deviation (MAD)374
Skewness-0.21494203
Sum1.9489533 × 108
Variance142848.1
MonotonicityNot monotonic
2025-07-17T06:02:52.324629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
19393 1901
19.5%
19790 1346
13.8%
19805 1207
12.4%
20304 1101
11.3%
19977 826
8.5%
20452 462
 
4.7%
20398 400
 
4.1%
20409 360
 
3.7%
20397 353
 
3.6%
20363 353
 
3.6%
Other values (8) 1449
14.8%
ValueCountFrequency (%)
19393 1901
19.5%
19687 67
 
0.7%
19690 105
 
1.1%
19790 1346
13.8%
19805 1207
12.4%
19930 320
 
3.3%
19977 826
8.5%
20304 1101
11.3%
20363 353
 
3.6%
20366 50
 
0.5%
ValueCountFrequency (%)
20452 462
4.7%
20436 230
2.4%
20416 295
3.0%
20409 360
3.7%
20398 400
4.1%
20397 353
3.6%
20378 206
2.1%
20368 176
 
1.8%
20366 50
 
0.5%
20363 353
3.6%

FL_NUMBER
Real number (ℝ)

Distinct4620
Distinct (%)47.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2505.3074
Minimum1
Maximum7431
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size152.5 KiB
2025-07-17T06:02:52.371122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile271
Q11037.25
median2144.5
Q33782.75
95-th percentile5635.15
Maximum7431
Range7430
Interquartile range (IQR)2745.5

Descriptive statistics

Standard deviation1740.4628
Coefficient of variation (CV)0.69471028
Kurtosis-0.88868511
Mean2505.3074
Median Absolute Deviation (MAD)1340.5
Skewness0.50876545
Sum24446790
Variance3029210.9
MonotonicityNot monotonic
2025-07-17T06:02:52.431105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
546 11
 
0.1%
922 8
 
0.1%
375 8
 
0.1%
62 8
 
0.1%
1517 8
 
0.1%
573 8
 
0.1%
2255 8
 
0.1%
1701 7
 
0.1%
5102 7
 
0.1%
529 7
 
0.1%
Other values (4610) 9678
99.2%
ValueCountFrequency (%)
1 3
< 0.1%
2 3
< 0.1%
3 3
< 0.1%
4 6
0.1%
5 3
< 0.1%
6 2
 
< 0.1%
7 2
 
< 0.1%
8 2
 
< 0.1%
9 3
< 0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
7431 1
< 0.1%
7395 1
< 0.1%
7393 1
< 0.1%
7389 1
< 0.1%
7387 1
< 0.1%
7385 1
< 0.1%
7381 1
< 0.1%
7378 1
< 0.1%
6960 1
< 0.1%
6957 1
< 0.1%

ORIGIN
Text

Distinct317
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size571.8 KiB
2025-07-17T06:02:52.601752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters29274
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique68 ?
Unique (%)0.7%

Sample

1st rowSAN
2nd rowCMH
3rd rowCVG
4th rowDEN
5th rowIAH
ValueCountFrequency (%)
atl 532
 
5.5%
ord 410
 
4.2%
dfw 394
 
4.0%
den 363
 
3.7%
clt 334
 
3.4%
lax 268
 
2.7%
phx 266
 
2.7%
las 215
 
2.2%
sea 210
 
2.2%
sfo 206
 
2.1%
Other values (307) 6560
67.2%
2025-07-17T06:02:52.796313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 3237
 
11.1%
L 2740
 
9.4%
S 2452
 
8.4%
D 2275
 
7.8%
T 1689
 
5.8%
C 1497
 
5.1%
O 1495
 
5.1%
M 1269
 
4.3%
F 1197
 
4.1%
P 1187
 
4.1%
Other values (16) 10236
35.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 29274
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 3237
 
11.1%
L 2740
 
9.4%
S 2452
 
8.4%
D 2275
 
7.8%
T 1689
 
5.8%
C 1497
 
5.1%
O 1495
 
5.1%
M 1269
 
4.3%
F 1197
 
4.1%
P 1187
 
4.1%
Other values (16) 10236
35.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 29274
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 3237
 
11.1%
L 2740
 
9.4%
S 2452
 
8.4%
D 2275
 
7.8%
T 1689
 
5.8%
C 1497
 
5.1%
O 1495
 
5.1%
M 1269
 
4.3%
F 1197
 
4.1%
P 1187
 
4.1%
Other values (16) 10236
35.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 29274
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 3237
 
11.1%
L 2740
 
9.4%
S 2452
 
8.4%
D 2275
 
7.8%
T 1689
 
5.8%
C 1497
 
5.1%
O 1495
 
5.1%
M 1269
 
4.3%
F 1197
 
4.1%
P 1187
 
4.1%
Other values (16) 10236
35.0%
Distinct312
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size668.0 KiB
2025-07-17T06:02:52.933033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length34
Median length29
Mean length13.094384
Min length8

Characters and Unicode

Total characters127775
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique69 ?
Unique (%)0.7%

Sample

1st rowSan Diego, CA
2nd rowColumbus, OH
3rd rowCincinnati, OH
4th rowDenver, CO
5th rowHouston, TX
ValueCountFrequency (%)
tx 1045
 
4.6%
ca 1041
 
4.6%
fl 832
 
3.7%
ga 565
 
2.5%
il 553
 
2.4%
atlanta 532
 
2.3%
chicago 528
 
2.3%
san 517
 
2.3%
ny 475
 
2.1%
nc 468
 
2.1%
Other values (385) 16099
71.1%
2025-07-17T06:02:53.118478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12897
 
10.1%
a 9768
 
7.6%
, 9758
 
7.6%
o 7153
 
5.6%
e 6663
 
5.2%
n 6340
 
5.0%
t 6256
 
4.9%
l 5725
 
4.5%
i 5008
 
3.9%
r 4549
 
3.6%
Other values (47) 53658
42.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 127775
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
12897
 
10.1%
a 9768
 
7.6%
, 9758
 
7.6%
o 7153
 
5.6%
e 6663
 
5.2%
n 6340
 
5.0%
t 6256
 
4.9%
l 5725
 
4.5%
i 5008
 
3.9%
r 4549
 
3.6%
Other values (47) 53658
42.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 127775
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
12897
 
10.1%
a 9768
 
7.6%
, 9758
 
7.6%
o 7153
 
5.6%
e 6663
 
5.2%
n 6340
 
5.0%
t 6256
 
4.9%
l 5725
 
4.5%
i 5008
 
3.9%
r 4549
 
3.6%
Other values (47) 53658
42.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 127775
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
12897
 
10.1%
a 9768
 
7.6%
, 9758
 
7.6%
o 7153
 
5.6%
e 6663
 
5.2%
n 6340
 
5.0%
t 6256
 
4.9%
l 5725
 
4.5%
i 5008
 
3.9%
r 4549
 
3.6%
Other values (47) 53658
42.0%

DEST
Text

Distinct307
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size571.8 KiB
2025-07-17T06:02:53.270024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters29274
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55 ?
Unique (%)0.6%

Sample

1st rowSFO
2nd rowORD
3rd rowORD
4th rowMCI
5th rowSFO
ValueCountFrequency (%)
atl 527
 
5.4%
den 410
 
4.2%
dfw 400
 
4.1%
ord 364
 
3.7%
lax 291
 
3.0%
clt 288
 
3.0%
phx 257
 
2.6%
dtw 240
 
2.5%
las 234
 
2.4%
sea 232
 
2.4%
Other values (297) 6515
66.8%
2025-07-17T06:02:53.461708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 3296
 
11.3%
L 2757
 
9.4%
S 2395
 
8.2%
D 2294
 
7.8%
T 1685
 
5.8%
C 1462
 
5.0%
O 1442
 
4.9%
M 1273
 
4.3%
F 1191
 
4.1%
P 1174
 
4.0%
Other values (16) 10305
35.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 29274
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 3296
 
11.3%
L 2757
 
9.4%
S 2395
 
8.2%
D 2294
 
7.8%
T 1685
 
5.8%
C 1462
 
5.0%
O 1442
 
4.9%
M 1273
 
4.3%
F 1191
 
4.1%
P 1174
 
4.0%
Other values (16) 10305
35.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 29274
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 3296
 
11.3%
L 2757
 
9.4%
S 2395
 
8.2%
D 2294
 
7.8%
T 1685
 
5.8%
C 1462
 
5.0%
O 1442
 
4.9%
M 1273
 
4.3%
F 1191
 
4.1%
P 1174
 
4.0%
Other values (16) 10305
35.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 29274
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 3296
 
11.3%
L 2757
 
9.4%
S 2395
 
8.2%
D 2294
 
7.8%
T 1685
 
5.8%
C 1462
 
5.0%
O 1442
 
4.9%
M 1273
 
4.3%
F 1191
 
4.1%
P 1174
 
4.0%
Other values (16) 10305
35.2%
Distinct302
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size667.9 KiB
2025-07-17T06:02:53.592415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length34
Median length29
Mean length13.088543
Min length8

Characters and Unicode

Total characters127718
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55 ?
Unique (%)0.6%

Sample

1st rowSan Francisco, CA
2nd rowChicago, IL
3rd rowChicago, IL
4th rowKansas City, MO
5th rowSan Francisco, CA
ValueCountFrequency (%)
ca 1059
 
4.7%
tx 1014
 
4.5%
fl 869
 
3.8%
ga 562
 
2.5%
atlanta 527
 
2.3%
il 496
 
2.2%
ny 484
 
2.1%
chicago 470
 
2.1%
san 456
 
2.0%
co 448
 
2.0%
Other values (381) 16284
71.8%
2025-07-17T06:02:53.773893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12911
 
10.1%
a 9767
 
7.6%
, 9758
 
7.6%
o 7049
 
5.5%
e 6796
 
5.3%
t 6385
 
5.0%
n 6242
 
4.9%
l 5734
 
4.5%
i 4866
 
3.8%
r 4704
 
3.7%
Other values (48) 53506
41.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 127718
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
12911
 
10.1%
a 9767
 
7.6%
, 9758
 
7.6%
o 7049
 
5.5%
e 6796
 
5.3%
t 6385
 
5.0%
n 6242
 
4.9%
l 5734
 
4.5%
i 4866
 
3.8%
r 4704
 
3.7%
Other values (48) 53506
41.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 127718
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
12911
 
10.1%
a 9767
 
7.6%
, 9758
 
7.6%
o 7049
 
5.5%
e 6796
 
5.3%
t 6385
 
5.0%
n 6242
 
4.9%
l 5734
 
4.5%
i 4866
 
3.8%
r 4704
 
3.7%
Other values (48) 53506
41.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 127718
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
12911
 
10.1%
a 9767
 
7.6%
, 9758
 
7.6%
o 7049
 
5.5%
e 6796
 
5.3%
t 6385
 
5.0%
n 6242
 
4.9%
l 5734
 
4.5%
i 4866
 
3.8%
r 4704
 
3.7%
Other values (48) 53506
41.9%

CRS_DEP_TIME
Real number (ℝ)

High correlation 

Distinct1058
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1336.3331
Minimum9
Maximum2359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size152.5 KiB
2025-07-17T06:02:53.822658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile605
Q1925
median1325
Q31735
95-th percentile2116.15
Maximum2359
Range2350
Interquartile range (IQR)810

Descriptive statistics

Standard deviation485.12895
Coefficient of variation (CV)0.36302997
Kurtosis-1.0269398
Mean1336.3331
Median Absolute Deviation (MAD)405
Skewness0.061249549
Sum13039938
Variance235350.1
MonotonicityNot monotonic
2025-07-17T06:02:53.875119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600 206
 
2.1%
700 162
 
1.7%
800 94
 
1.0%
900 72
 
0.7%
1100 66
 
0.7%
630 63
 
0.6%
830 61
 
0.6%
1600 60
 
0.6%
730 57
 
0.6%
1700 55
 
0.6%
Other values (1048) 8862
90.8%
ValueCountFrequency (%)
9 1
< 0.1%
14 1
< 0.1%
15 2
< 0.1%
20 1
< 0.1%
30 1
< 0.1%
35 1
< 0.1%
40 2
< 0.1%
44 1
< 0.1%
45 2
< 0.1%
55 2
< 0.1%
ValueCountFrequency (%)
2359 15
0.2%
2357 1
 
< 0.1%
2355 5
 
0.1%
2354 2
 
< 0.1%
2353 1
 
< 0.1%
2350 2
 
< 0.1%
2349 1
 
< 0.1%
2348 1
 
< 0.1%
2346 1
 
< 0.1%
2345 6
 
0.1%

DEP_TIME
Real number (ℝ)

High correlation 

Distinct1184
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1339.6814
Minimum5
Maximum2359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size152.5 KiB
2025-07-17T06:02:53.927120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile602
Q1926
median1331
Q31741
95-th percentile2130.15
Maximum2359
Range2354
Interquartile range (IQR)815

Descriptive statistics

Standard deviation498.72675
Coefficient of variation (CV)0.37227266
Kurtosis-0.96495812
Mean1339.6814
Median Absolute Deviation (MAD)408
Skewness0.016165344
Sum13072611
Variance248728.38
MonotonicityNot monotonic
2025-07-17T06:02:53.978020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
555 29
 
0.3%
655 27
 
0.3%
559 27
 
0.3%
558 27
 
0.3%
556 25
 
0.3%
554 25
 
0.3%
656 25
 
0.3%
1225 23
 
0.2%
1725 22
 
0.2%
1054 21
 
0.2%
Other values (1174) 9507
97.4%
ValueCountFrequency (%)
5 1
< 0.1%
6 1
< 0.1%
8 1
< 0.1%
10 1
< 0.1%
12 1
< 0.1%
13 1
< 0.1%
16 2
< 0.1%
17 2
< 0.1%
20 1
< 0.1%
24 2
< 0.1%
ValueCountFrequency (%)
2359 3
< 0.1%
2358 2
< 0.1%
2357 1
 
< 0.1%
2356 1
 
< 0.1%
2355 2
< 0.1%
2354 3
< 0.1%
2353 2
< 0.1%
2350 2
< 0.1%
2349 4
< 0.1%
2348 1
 
< 0.1%

DEP_DELAY
Real number (ℝ)

High correlation  Zeros 

Distinct285
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.01363
Minimum-28
Maximum1130
Zeros463
Zeros (%)4.7%
Negative6015
Negative (%)61.6%
Memory size152.5 KiB
2025-07-17T06:02:54.026263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-28
5-th percentile-10
Q1-6
median-2
Q36
95-th percentile71
Maximum1130
Range1158
Interquartile range (IQR)12

Descriptive statistics

Standard deviation48.886139
Coefficient of variation (CV)4.8819599
Kurtosis173.78012
Mean10.01363
Median Absolute Deviation (MAD)4
Skewness10.508127
Sum97713
Variance2389.8546
MonotonicityNot monotonic
2025-07-17T06:02:54.078329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5 828
 
8.5%
-4 756
 
7.7%
-2 663
 
6.8%
-3 662
 
6.8%
-6 655
 
6.7%
-1 571
 
5.9%
-7 471
 
4.8%
0 463
 
4.7%
-8 402
 
4.1%
-9 305
 
3.1%
Other values (275) 3982
40.8%
ValueCountFrequency (%)
-28 1
 
< 0.1%
-26 1
 
< 0.1%
-25 4
 
< 0.1%
-24 4
 
< 0.1%
-23 1
 
< 0.1%
-22 1
 
< 0.1%
-21 2
 
< 0.1%
-20 1
 
< 0.1%
-19 4
 
< 0.1%
-18 12
0.1%
ValueCountFrequency (%)
1130 1
< 0.1%
1096 1
< 0.1%
1069 1
< 0.1%
1022 1
< 0.1%
951 1
< 0.1%
920 1
< 0.1%
852 1
< 0.1%
825 1
< 0.1%
803 1
< 0.1%
752 1
< 0.1%

TAXI_OUT
Real number (ℝ)

Distinct85
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.541299
Minimum2
Maximum158
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size152.5 KiB
2025-07-17T06:02:54.120933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8
Q111
median14
Q319
95-th percentile33
Maximum158
Range156
Interquartile range (IQR)8

Descriptive statistics

Standard deviation9.0154733
Coefficient of variation (CV)0.54502812
Kurtosis21.423444
Mean16.541299
Median Absolute Deviation (MAD)4
Skewness3.2458959
Sum161410
Variance81.278759
MonotonicityNot monotonic
2025-07-17T06:02:54.170891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 789
 
8.1%
11 784
 
8.0%
12 778
 
8.0%
14 710
 
7.3%
10 698
 
7.2%
15 676
 
6.9%
9 542
 
5.6%
16 532
 
5.5%
17 452
 
4.6%
18 388
 
4.0%
Other values (75) 3409
34.9%
ValueCountFrequency (%)
2 1
 
< 0.1%
3 5
 
0.1%
4 8
 
0.1%
5 26
 
0.3%
6 99
 
1.0%
7 215
 
2.2%
8 368
3.8%
9 542
5.6%
10 698
7.2%
11 784
8.0%
ValueCountFrequency (%)
158 1
< 0.1%
136 1
< 0.1%
106 1
< 0.1%
105 2
< 0.1%
92 1
< 0.1%
90 1
< 0.1%
89 2
< 0.1%
85 2
< 0.1%
83 1
< 0.1%
82 1
< 0.1%

WHEELS_OFF
Real number (ℝ)

High correlation 

Distinct1190
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1362.7527
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size152.5 KiB
2025-07-17T06:02:54.230951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile617
Q1941
median1344
Q31756
95-th percentile2143.15
Maximum2400
Range2399
Interquartile range (IQR)815

Descriptive statistics

Standard deviation500.66993
Coefficient of variation (CV)0.36739602
Kurtosis-0.90052907
Mean1362.7527
Median Absolute Deviation (MAD)407
Skewness-0.016190699
Sum13297741
Variance250670.38
MonotonicityNot monotonic
2025-07-17T06:02:54.278740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1022 24
 
0.2%
610 23
 
0.2%
1621 21
 
0.2%
740 20
 
0.2%
913 19
 
0.2%
1201 19
 
0.2%
617 19
 
0.2%
707 18
 
0.2%
717 18
 
0.2%
1029 18
 
0.2%
Other values (1180) 9559
98.0%
ValueCountFrequency (%)
1 2
< 0.1%
2 2
< 0.1%
4 1
 
< 0.1%
6 1
 
< 0.1%
8 1
 
< 0.1%
9 2
< 0.1%
10 3
< 0.1%
11 1
 
< 0.1%
12 2
< 0.1%
13 1
 
< 0.1%
ValueCountFrequency (%)
2400 2
 
< 0.1%
2359 2
 
< 0.1%
2358 5
0.1%
2357 1
 
< 0.1%
2356 3
< 0.1%
2354 4
< 0.1%
2353 1
 
< 0.1%
2351 1
 
< 0.1%
2350 2
 
< 0.1%
2348 6
0.1%

WHEELS_ON
Real number (ℝ)

High correlation 

Distinct1248
Distinct (%)12.8%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1476.5719
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size152.5 KiB
2025-07-17T06:02:54.326085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile685.65
Q11100
median1513
Q31914
95-th percentile2251
Maximum2400
Range2399
Interquartile range (IQR)814

Descriptive statistics

Standard deviation524.65085
Coefficient of variation (CV)0.35531684
Kurtosis-0.44881106
Mean1476.5719
Median Absolute Deviation (MAD)407
Skewness-0.3229741
Sum14402482
Variance275258.51
MonotonicityNot monotonic
2025-07-17T06:02:54.373425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1317 22
 
0.2%
1451 19
 
0.2%
2215 19
 
0.2%
1241 18
 
0.2%
1843 18
 
0.2%
1337 18
 
0.2%
1203 18
 
0.2%
1516 18
 
0.2%
1903 18
 
0.2%
1832 18
 
0.2%
Other values (1238) 9568
98.1%
ValueCountFrequency (%)
1 6
0.1%
2 4
< 0.1%
3 3
< 0.1%
4 5
0.1%
6 4
< 0.1%
8 2
 
< 0.1%
9 4
< 0.1%
10 3
< 0.1%
11 1
 
< 0.1%
12 2
 
< 0.1%
ValueCountFrequency (%)
2400 7
0.1%
2359 5
0.1%
2358 1
 
< 0.1%
2357 5
0.1%
2356 4
< 0.1%
2355 6
0.1%
2354 6
0.1%
2353 8
0.1%
2352 4
< 0.1%
2351 6
0.1%

TAXI_IN
Real number (ℝ)

Distinct64
Distinct (%)0.7%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean7.5596678
Minimum1
Maximum84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size152.5 KiB
2025-07-17T06:02:54.426150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median6
Q39
95-th percentile17
Maximum84
Range83
Interquartile range (IQR)5

Descriptive statistics

Standard deviation6.0813867
Coefficient of variation (CV)0.80445157
Kurtosis28.885577
Mean7.5596678
Median Absolute Deviation (MAD)2
Skewness4.1784862
Sum73737
Variance36.983264
MonotonicityNot monotonic
2025-07-17T06:02:54.473692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 1551
15.9%
5 1351
13.8%
6 1189
12.2%
3 1076
11.0%
7 896
9.2%
8 693
7.1%
9 546
 
5.6%
10 422
 
4.3%
2 331
 
3.4%
11 295
 
3.0%
Other values (54) 1404
14.4%
ValueCountFrequency (%)
1 16
 
0.2%
2 331
 
3.4%
3 1076
11.0%
4 1551
15.9%
5 1351
13.8%
6 1189
12.2%
7 896
9.2%
8 693
7.1%
9 546
 
5.6%
10 422
 
4.3%
ValueCountFrequency (%)
84 1
 
< 0.1%
77 2
< 0.1%
76 2
< 0.1%
75 1
 
< 0.1%
74 1
 
< 0.1%
72 1
 
< 0.1%
67 2
< 0.1%
65 1
 
< 0.1%
64 1
 
< 0.1%
61 3
< 0.1%

CRS_ARR_TIME
Real number (ℝ)

High correlation 

Distinct1171
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1502.8471
Minimum1
Maximum2359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size152.5 KiB
2025-07-17T06:02:54.535914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile729
Q11117
median1525.5
Q31923.75
95-th percentile2256
Maximum2359
Range2358
Interquartile range (IQR)806.75

Descriptive statistics

Standard deviation507.72583
Coefficient of variation (CV)0.33784264
Kurtosis-0.50218927
Mean1502.8471
Median Absolute Deviation (MAD)404.5
Skewness-0.2806888
Sum14664782
Variance257785.52
MonotonicityNot monotonic
2025-07-17T06:02:54.583216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1140 33
 
0.3%
1900 32
 
0.3%
1825 32
 
0.3%
1130 32
 
0.3%
2030 32
 
0.3%
2005 32
 
0.3%
1910 31
 
0.3%
1235 30
 
0.3%
1440 29
 
0.3%
1725 29
 
0.3%
Other values (1161) 9446
96.8%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 3
< 0.1%
3 1
 
< 0.1%
5 4
< 0.1%
7 1
 
< 0.1%
8 2
 
< 0.1%
9 3
< 0.1%
10 3
< 0.1%
11 5
0.1%
12 3
< 0.1%
ValueCountFrequency (%)
2359 25
0.3%
2358 10
 
0.1%
2357 12
0.1%
2356 9
 
0.1%
2355 20
0.2%
2354 8
 
0.1%
2353 6
 
0.1%
2352 11
0.1%
2351 5
 
0.1%
2350 12
0.1%

ARR_TIME
Real number (ℝ)

High correlation 

Distinct1237
Distinct (%)12.7%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1479.6288
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size152.5 KiB
2025-07-17T06:02:54.630656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile659
Q11102
median1517
Q31918
95-th percentile2253
Maximum2400
Range2399
Interquartile range (IQR)816

Descriptive statistics

Standard deviation529.90085
Coefficient of variation (CV)0.35813094
Kurtosis-0.36393678
Mean1479.6288
Median Absolute Deviation (MAD)408
Skewness-0.36670321
Sum14432299
Variance280794.91
MonotonicityNot monotonic
2025-07-17T06:02:54.677881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1801 22
 
0.2%
1630 21
 
0.2%
1845 19
 
0.2%
1850 19
 
0.2%
1455 18
 
0.2%
2120 18
 
0.2%
1613 18
 
0.2%
1606 18
 
0.2%
1936 18
 
0.2%
1915 17
 
0.2%
Other values (1227) 9566
98.0%
ValueCountFrequency (%)
1 4
< 0.1%
2 6
0.1%
3 3
< 0.1%
4 4
< 0.1%
5 7
0.1%
6 2
 
< 0.1%
7 7
0.1%
8 4
< 0.1%
9 5
0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
2400 5
0.1%
2359 3
 
< 0.1%
2358 7
0.1%
2357 11
0.1%
2356 9
0.1%
2355 2
 
< 0.1%
2354 6
0.1%
2353 7
0.1%
2352 9
0.1%
2351 6
0.1%

ARR_DELAY
Real number (ℝ)

High correlation  Zeros 

Distinct312
Distinct (%)3.2%
Missing19
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean3.9777185
Minimum-75
Maximum1163
Zeros173
Zeros (%)1.8%
Negative6357
Negative (%)65.1%
Memory size152.5 KiB
2025-07-17T06:02:54.736567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-75
5-th percentile-28
Q1-16
median-7
Q36
95-th percentile72
Maximum1163
Range1238
Interquartile range (IQR)22

Descriptive statistics

Standard deviation50.820267
Coefficient of variation (CV)12.776235
Kurtosis152.83043
Mean3.9777185
Median Absolute Deviation (MAD)10
Skewness9.5272045
Sum38739
Variance2582.6995
MonotonicityNot monotonic
2025-07-17T06:02:54.788910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-10 294
 
3.0%
-11 289
 
3.0%
-14 287
 
2.9%
-9 286
 
2.9%
-8 286
 
2.9%
-12 273
 
2.8%
-13 272
 
2.8%
-16 265
 
2.7%
-15 264
 
2.7%
-17 264
 
2.7%
Other values (302) 6959
71.3%
ValueCountFrequency (%)
-75 1
 
< 0.1%
-63 1
 
< 0.1%
-58 1
 
< 0.1%
-57 2
< 0.1%
-56 1
 
< 0.1%
-55 1
 
< 0.1%
-54 1
 
< 0.1%
-53 1
 
< 0.1%
-52 2
< 0.1%
-51 3
< 0.1%
ValueCountFrequency (%)
1163 1
< 0.1%
1093 1
< 0.1%
1072 1
< 0.1%
1008 1
< 0.1%
944 1
< 0.1%
887 1
< 0.1%
861 1
< 0.1%
808 1
< 0.1%
793 1
< 0.1%
751 1
< 0.1%

CANCELLED
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size571.8 KiB
0.0
9758 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters29274
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 9758
100.0%

Length

2025-07-17T06:02:54.836174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T06:02:54.855648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 9758
100.0%

Most occurring characters

ValueCountFrequency (%)
0 19516
66.7%
. 9758
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 29274
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 19516
66.7%
. 9758
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 29274
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 19516
66.7%
. 9758
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 29274
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 19516
66.7%
. 9758
33.3%

CANCELLATION_CODE
Unsupported

Missing  Rejected  Unsupported 

Missing9758
Missing (%)100.0%
Memory size152.5 KiB

DIVERTED
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size571.8 KiB
0.0
9739 
1.0
 
19

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters29274
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 9739
99.8%
1.0 19
 
0.2%

Length

2025-07-17T06:02:54.891723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T06:02:54.917260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 9739
99.8%
1.0 19
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 19497
66.6%
. 9758
33.3%
1 19
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 29274
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 19497
66.6%
. 9758
33.3%
1 19
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 29274
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 19497
66.6%
. 9758
33.3%
1 19
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 29274
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 19497
66.6%
. 9758
33.3%
1 19
 
0.1%

CRS_ELAPSED_TIME
Real number (ℝ)

High correlation 

Distinct391
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean142.83296
Minimum25
Maximum680
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size152.5 KiB
2025-07-17T06:02:55.223076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile63
Q190
median125
Q3174
95-th percentile302.15
Maximum680
Range655
Interquartile range (IQR)84

Descriptive statistics

Standard deviation72.696474
Coefficient of variation (CV)0.50896148
Kurtosis2.6956922
Mean142.83296
Median Absolute Deviation (MAD)40
Skewness1.4476616
Sum1393764
Variance5284.7773
MonotonicityNot monotonic
2025-07-17T06:02:55.284169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 201
 
2.1%
85 190
 
1.9%
80 164
 
1.7%
75 149
 
1.5%
70 145
 
1.5%
110 140
 
1.4%
95 138
 
1.4%
100 137
 
1.4%
65 129
 
1.3%
105 121
 
1.2%
Other values (381) 8244
84.5%
ValueCountFrequency (%)
25 1
 
< 0.1%
31 1
 
< 0.1%
34 1
 
< 0.1%
35 3
 
< 0.1%
36 6
 
0.1%
37 7
0.1%
38 3
 
< 0.1%
39 5
 
0.1%
40 16
0.2%
41 7
0.1%
ValueCountFrequency (%)
680 1
< 0.1%
622 1
< 0.1%
585 1
< 0.1%
580 1
< 0.1%
575 2
< 0.1%
558 1
< 0.1%
548 1
< 0.1%
525 1
< 0.1%
521 2
< 0.1%
484 1
< 0.1%

ELAPSED_TIME
Real number (ℝ)

High correlation 

Distinct406
Distinct (%)4.2%
Missing19
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean136.85645
Minimum26
Maximum630
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size152.5 KiB
2025-07-17T06:02:55.344157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile56
Q183
median119
Q3168
95-th percentile292
Maximum630
Range604
Interquartile range (IQR)85

Descriptive statistics

Standard deviation72.731067
Coefficient of variation (CV)0.53144054
Kurtosis2.6405568
Mean136.85645
Median Absolute Deviation (MAD)40
Skewness1.4325807
Sum1332845
Variance5289.8081
MonotonicityNot monotonic
2025-07-17T06:02:55.404459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75 96
 
1.0%
76 92
 
0.9%
80 90
 
0.9%
69 88
 
0.9%
105 86
 
0.9%
72 85
 
0.9%
82 85
 
0.9%
77 85
 
0.9%
84 84
 
0.9%
99 83
 
0.9%
Other values (396) 8865
90.8%
ValueCountFrequency (%)
26 1
 
< 0.1%
28 1
 
< 0.1%
29 2
 
< 0.1%
31 1
 
< 0.1%
32 7
0.1%
33 3
 
< 0.1%
34 4
 
< 0.1%
35 5
0.1%
36 10
0.1%
37 8
0.1%
ValueCountFrequency (%)
630 1
< 0.1%
587 1
< 0.1%
585 1
< 0.1%
581 1
< 0.1%
578 1
< 0.1%
574 1
< 0.1%
554 1
< 0.1%
534 1
< 0.1%
527 1
< 0.1%
521 1
< 0.1%

AIR_TIME
Real number (ℝ)

High correlation 

Distinct378
Distinct (%)3.9%
Missing19
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean112.76301
Minimum14
Maximum610
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size152.5 KiB
2025-07-17T06:02:55.465061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile35
Q161
median95
Q3142
95-th percentile268
Maximum610
Range596
Interquartile range (IQR)81

Descriptive statistics

Standard deviation70.914231
Coefficient of variation (CV)0.62887846
Kurtosis2.6874162
Mean112.76301
Median Absolute Deviation (MAD)39
Skewness1.4590352
Sum1098199
Variance5028.8282
MonotonicityNot monotonic
2025-07-17T06:02:55.525974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57 102
 
1.0%
63 98
 
1.0%
53 94
 
1.0%
61 88
 
0.9%
43 86
 
0.9%
68 86
 
0.9%
59 85
 
0.9%
50 85
 
0.9%
52 85
 
0.9%
85 85
 
0.9%
Other values (368) 8845
90.6%
ValueCountFrequency (%)
14 1
 
< 0.1%
15 1
 
< 0.1%
16 1
 
< 0.1%
17 3
 
< 0.1%
18 5
 
0.1%
19 7
 
0.1%
20 12
0.1%
21 25
0.3%
22 22
0.2%
23 18
0.2%
ValueCountFrequency (%)
610 1
< 0.1%
564 1
< 0.1%
557 1
< 0.1%
550 1
< 0.1%
544 1
< 0.1%
522 1
< 0.1%
518 1
< 0.1%
502 2
< 0.1%
465 1
< 0.1%
462 1
< 0.1%

DISTANCE
Real number (ℝ)

High correlation 

Distinct1306
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean815.00676
Minimum31
Maximum5095
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size152.5 KiB
2025-07-17T06:02:55.586377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile168.85
Q1371
median651
Q31046
95-th percentile2153
Maximum5095
Range5064
Interquartile range (IQR)675

Descriptive statistics

Standard deviation601.55959
Coefficient of variation (CV)0.7381038
Kurtosis3.2607053
Mean815.00676
Median Absolute Deviation (MAD)321
Skewness1.5551843
Sum7952836
Variance361873.94
MonotonicityNot monotonic
2025-07-17T06:02:55.648878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
337 59
 
0.6%
296 54
 
0.6%
214 45
 
0.5%
224 45
 
0.5%
862 43
 
0.4%
733 42
 
0.4%
399 41
 
0.4%
391 41
 
0.4%
588 36
 
0.4%
957 36
 
0.4%
Other values (1296) 9316
95.5%
ValueCountFrequency (%)
31 1
 
< 0.1%
67 1
 
< 0.1%
68 4
 
< 0.1%
73 9
0.1%
74 3
 
< 0.1%
75 12
0.1%
76 3
 
< 0.1%
77 3
 
< 0.1%
82 3
 
< 0.1%
83 8
0.1%
ValueCountFrequency (%)
5095 2
< 0.1%
4983 2
< 0.1%
4962 1
 
< 0.1%
4502 1
 
< 0.1%
4243 3
< 0.1%
3972 1
 
< 0.1%
3904 3
< 0.1%
3801 2
< 0.1%
3784 3
< 0.1%
3711 1
 
< 0.1%

DELAY_DUE_CARRIER
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct161
Distinct (%)9.3%
Missing8021
Missing (%)82.2%
Infinite0
Infinite (%)0.0%
Mean23.559585
Minimum0
Maximum1130
Zeros762
Zeros (%)7.8%
Negative0
Negative (%)0.0%
Memory size152.5 KiB
2025-07-17T06:02:55.710475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q322
95-th percentile97.4
Maximum1130
Range1130
Interquartile range (IQR)22

Descriptive statistics

Standard deviation69.132695
Coefficient of variation (CV)2.9343765
Kurtosis126.99592
Mean23.559585
Median Absolute Deviation (MAD)4
Skewness9.8124804
Sum40923
Variance4779.3295
MonotonicityNot monotonic
2025-07-17T06:02:55.769651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 762
 
7.8%
1 40
 
0.4%
6 34
 
0.3%
8 32
 
0.3%
3 31
 
0.3%
5 29
 
0.3%
2 29
 
0.3%
4 29
 
0.3%
17 28
 
0.3%
13 27
 
0.3%
Other values (151) 696
 
7.1%
(Missing) 8021
82.2%
ValueCountFrequency (%)
0 762
7.8%
1 40
 
0.4%
2 29
 
0.3%
3 31
 
0.3%
4 29
 
0.3%
5 29
 
0.3%
6 34
 
0.3%
7 25
 
0.3%
8 32
 
0.3%
9 22
 
0.2%
ValueCountFrequency (%)
1130 1
< 0.1%
1093 1
< 0.1%
998 1
< 0.1%
887 1
< 0.1%
793 1
< 0.1%
706 1
< 0.1%
683 1
< 0.1%
469 1
< 0.1%
445 1
< 0.1%
289 1
< 0.1%

DELAY_DUE_WEATHER
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct60
Distinct (%)3.5%
Missing8021
Missing (%)82.2%
Infinite0
Infinite (%)0.0%
Mean3.7063903
Minimum0
Maximum751
Zeros1640
Zeros (%)16.8%
Negative0
Negative (%)0.0%
Memory size152.5 KiB
2025-07-17T06:02:55.827345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum751
Range751
Interquartile range (IQR)0

Descriptive statistics

Standard deviation33.492453
Coefficient of variation (CV)9.0364073
Kurtosis328.52095
Mean3.7063903
Median Absolute Deviation (MAD)0
Skewness16.810881
Sum6438
Variance1121.7444
MonotonicityNot monotonic
2025-07-17T06:02:55.890288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1640
 
16.8%
6 5
 
0.1%
10 5
 
0.1%
24 4
 
< 0.1%
39 4
 
< 0.1%
15 3
 
< 0.1%
26 3
 
< 0.1%
2 3
 
< 0.1%
44 2
 
< 0.1%
76 2
 
< 0.1%
Other values (50) 66
 
0.7%
(Missing) 8021
82.2%
ValueCountFrequency (%)
0 1640
16.8%
2 3
 
< 0.1%
3 2
 
< 0.1%
4 1
 
< 0.1%
5 2
 
< 0.1%
6 5
 
0.1%
7 1
 
< 0.1%
8 2
 
< 0.1%
10 5
 
0.1%
11 2
 
< 0.1%
ValueCountFrequency (%)
751 1
< 0.1%
668 1
< 0.1%
667 1
< 0.1%
312 1
< 0.1%
256 1
< 0.1%
235 1
< 0.1%
227 1
< 0.1%
176 1
< 0.1%
155 1
< 0.1%
153 1
< 0.1%

DELAY_DUE_NAS
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct111
Distinct (%)6.4%
Missing8021
Missing (%)82.2%
Infinite0
Infinite (%)0.0%
Mean13.095567
Minimum0
Maximum944
Zeros894
Zeros (%)9.2%
Negative0
Negative (%)0.0%
Memory size152.5 KiB
2025-07-17T06:02:55.953392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q317
95-th percentile52
Maximum944
Range944
Interquartile range (IQR)17

Descriptive statistics

Standard deviation35.043309
Coefficient of variation (CV)2.6759673
Kurtosis298.59972
Mean13.095567
Median Absolute Deviation (MAD)0
Skewness13.092185
Sum22747
Variance1228.0335
MonotonicityNot monotonic
2025-07-17T06:02:56.013431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 894
 
9.2%
1 41
 
0.4%
2 38
 
0.4%
15 34
 
0.3%
3 34
 
0.3%
5 34
 
0.3%
17 32
 
0.3%
7 30
 
0.3%
4 29
 
0.3%
6 26
 
0.3%
Other values (101) 545
 
5.6%
(Missing) 8021
82.2%
ValueCountFrequency (%)
0 894
9.2%
1 41
 
0.4%
2 38
 
0.4%
3 34
 
0.3%
4 29
 
0.3%
5 34
 
0.3%
6 26
 
0.3%
7 30
 
0.3%
8 23
 
0.2%
9 25
 
0.3%
ValueCountFrequency (%)
944 1
< 0.1%
318 1
< 0.1%
287 1
< 0.1%
253 1
< 0.1%
242 1
< 0.1%
235 1
< 0.1%
230 1
< 0.1%
221 1
< 0.1%
210 1
< 0.1%
196 1
< 0.1%

DELAY_DUE_SECURITY
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct12
Distinct (%)0.7%
Missing8021
Missing (%)82.2%
Infinite0
Infinite (%)0.0%
Mean0.19976972
Minimum0
Maximum76
Zeros1724
Zeros (%)17.7%
Negative0
Negative (%)0.0%
Memory size152.5 KiB
2025-07-17T06:02:56.059286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum76
Range76
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.8569666
Coefficient of variation (CV)14.3013
Kurtosis422.4753
Mean0.19976972
Median Absolute Deviation (MAD)0
Skewness19.104438
Sum347
Variance8.162258
MonotonicityNot monotonic
2025-07-17T06:02:56.103754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 1724
 
17.7%
9 2
 
< 0.1%
16 2
 
< 0.1%
76 1
 
< 0.1%
24 1
 
< 0.1%
39 1
 
< 0.1%
30 1
 
< 0.1%
25 1
 
< 0.1%
59 1
 
< 0.1%
19 1
 
< 0.1%
Other values (2) 2
 
< 0.1%
(Missing) 8021
82.2%
ValueCountFrequency (%)
0 1724
17.7%
9 2
 
< 0.1%
10 1
 
< 0.1%
15 1
 
< 0.1%
16 2
 
< 0.1%
19 1
 
< 0.1%
24 1
 
< 0.1%
25 1
 
< 0.1%
30 1
 
< 0.1%
39 1
 
< 0.1%
ValueCountFrequency (%)
76 1
< 0.1%
59 1
< 0.1%
39 1
< 0.1%
30 1
< 0.1%
25 1
< 0.1%
24 1
< 0.1%
19 1
< 0.1%
16 2
< 0.1%
15 1
< 0.1%
10 1
< 0.1%

DELAY_DUE_LATE_AIRCRAFT
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct184
Distinct (%)10.6%
Missing8021
Missing (%)82.2%
Infinite0
Infinite (%)0.0%
Mean27.899252
Minimum0
Maximum1069
Zeros840
Zeros (%)8.6%
Negative0
Negative (%)0.0%
Memory size152.5 KiB
2025-07-17T06:02:56.160021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q334
95-th percentile132
Maximum1069
Range1069
Interquartile range (IQR)34

Descriptive statistics

Standard deviation56.366617
Coefficient of variation (CV)2.020363
Kurtosis84.140137
Mean27.899252
Median Absolute Deviation (MAD)3
Skewness6.4762384
Sum48461
Variance3177.1955
MonotonicityNot monotonic
2025-07-17T06:02:56.221052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 840
 
8.6%
20 23
 
0.2%
15 23
 
0.2%
18 20
 
0.2%
19 20
 
0.2%
16 18
 
0.2%
26 18
 
0.2%
4 18
 
0.2%
14 17
 
0.2%
3 15
 
0.2%
Other values (174) 725
 
7.4%
(Missing) 8021
82.2%
ValueCountFrequency (%)
0 840
8.6%
1 11
 
0.1%
2 14
 
0.1%
3 15
 
0.2%
4 18
 
0.2%
5 10
 
0.1%
6 14
 
0.1%
7 13
 
0.1%
8 15
 
0.2%
9 15
 
0.2%
ValueCountFrequency (%)
1069 1
< 0.1%
625 1
< 0.1%
587 1
< 0.1%
485 1
< 0.1%
367 1
< 0.1%
313 1
< 0.1%
290 1
< 0.1%
289 1
< 0.1%
277 1
< 0.1%
262 1
< 0.1%

Interactions

2025-07-17T06:02:50.407130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:31.196377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-17T06:02:34.069580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:34.939109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-17T06:02:37.018814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:37.906882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-17T06:02:33.901254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:34.773757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:35.666674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:36.831887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:37.740412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:38.611508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:39.461395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:40.549403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:41.444935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:42.353776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:43.322462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:44.471456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:45.373899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:46.253235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:47.144692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:48.373533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:49.271224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:50.222842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:51.211412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:31.972140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:33.043639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:33.947208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:34.819783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:35.709483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:36.876232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:37.780672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:38.653308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:39.505230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:40.596127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:41.488284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:42.402523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:43.368848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:44.520585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:45.423470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:46.299038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:47.199410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:48.421427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:49.318646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:50.269880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:51.252503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:32.174984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:33.091208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:33.989671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:34.858468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:35.964454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:36.922877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:37.825362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:38.697483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:39.766905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:40.639824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:41.527738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:42.442802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:43.417398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:44.560325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:45.465647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:46.342702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:47.235943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:48.473981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:49.357693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:50.312216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:51.297121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:32.217077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:33.135152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:34.029513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:34.901284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:36.009151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:36.968903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:37.870539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:38.735747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:39.802957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:40.683100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:41.567881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:42.486420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:43.460746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:44.605337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:45.510289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:46.383451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:47.285168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:48.520781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:49.400622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T06:02:50.361620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-07-17T06:02:56.278158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AIRLINEAIRLINE_CODEAIRLINE_DOTAIR_TIMEARR_DELAYARR_TIMECRS_ARR_TIMECRS_DEP_TIMECRS_ELAPSED_TIMEDELAY_DUE_CARRIERDELAY_DUE_LATE_AIRCRAFTDELAY_DUE_NASDELAY_DUE_SECURITYDELAY_DUE_WEATHERDEP_DELAYDEP_TIMEDISTANCEDIVERTEDDOT_CODEELAPSED_TIMEFL_NUMBERTAXI_INTAXI_OUTWHEELS_OFFWHEELS_ON
AIRLINE1.0001.0001.0000.1700.0240.0510.0510.0530.1800.0000.0990.0470.0380.0000.0230.0490.1830.0350.9990.1760.4550.0700.1010.0510.050
AIRLINE_CODE1.0001.0001.0000.1700.0240.0510.0510.0530.1800.0000.0990.0470.0380.0000.0230.0490.1830.0350.9990.1760.4550.0700.1010.0510.050
AIRLINE_DOT1.0001.0001.0000.1700.0240.0510.0510.0530.1800.0000.0990.0470.0380.0000.0230.0490.1830.0350.9990.1760.4550.0700.1010.0510.050
AIR_TIME0.1700.1700.1701.0000.0520.0250.036-0.0460.9830.037-0.0880.144-0.0180.0010.099-0.0510.9861.000-0.0790.979-0.3380.1330.063-0.0580.031
ARR_DELAY0.0240.0240.0240.0521.0000.1100.1070.125-0.0110.1710.4050.050-0.0210.1010.6500.1630.0231.000-0.0530.117-0.0450.1150.2670.1700.117
ARR_TIME0.0510.0510.0510.0250.1101.0000.9040.7400.020-0.0650.1250.0150.009-0.0020.1310.7620.0320.043-0.0060.029-0.007-0.0390.0400.7760.977
CRS_ARR_TIME0.0510.0510.0510.0360.1070.9041.0000.8020.033-0.0600.185-0.013-0.0060.0350.1370.7990.0460.000-0.0020.037-0.021-0.0380.0300.8100.911
CRS_DEP_TIME0.0530.0530.053-0.0460.1250.7400.8021.000-0.052-0.0530.218-0.0500.0120.0340.1500.971-0.0390.0000.014-0.046-0.010-0.0570.0220.9560.759
CRS_ELAPSED_TIME0.1800.1800.1800.983-0.0110.0200.033-0.0521.0000.055-0.0770.099-0.021-0.0030.093-0.0560.9790.000-0.0360.974-0.3110.1710.107-0.0630.026
DELAY_DUE_CARRIER0.0000.0000.0000.0370.171-0.065-0.060-0.0530.0551.000-0.281-0.330-0.065-0.2330.272-0.0350.0661.000-0.099-0.0080.007-0.122-0.093-0.048-0.072
DELAY_DUE_LATE_AIRCRAFT0.0990.0990.099-0.0880.4050.1250.1850.218-0.077-0.2811.000-0.250-0.036-0.0250.4880.262-0.0681.000-0.042-0.1470.023-0.102-0.1500.2410.129
DELAY_DUE_NAS0.0470.0470.0470.1440.0500.015-0.013-0.0500.099-0.330-0.2501.000-0.045-0.006-0.325-0.0710.0821.0000.1810.301-0.0560.3100.447-0.0410.017
DELAY_DUE_SECURITY0.0380.0380.038-0.018-0.0210.009-0.0060.012-0.021-0.065-0.036-0.0451.000-0.021-0.0010.013-0.0111.0000.013-0.029-0.039-0.043-0.0340.0130.007
DELAY_DUE_WEATHER0.0000.0000.0000.0010.101-0.0020.0350.034-0.003-0.233-0.025-0.006-0.0211.0000.0860.005-0.0001.0000.0420.011-0.0110.0090.0480.0100.017
DEP_DELAY0.0230.0230.0230.0990.6500.1310.1370.1500.0930.2720.488-0.325-0.0010.0861.0000.1950.1090.101-0.1900.098-0.083-0.0590.0270.1930.139
DEP_TIME0.0490.0490.049-0.0510.1630.7620.7990.971-0.056-0.0350.262-0.0710.0130.0050.1951.000-0.0440.0000.009-0.049-0.006-0.0590.0290.9850.782
DISTANCE0.1830.1830.1830.9860.0230.0320.046-0.0390.9790.066-0.0680.082-0.011-0.0000.109-0.0441.0000.013-0.1010.962-0.3550.1180.048-0.0530.038
DIVERTED0.0350.0350.0351.0001.0000.0430.0000.0000.0001.0001.0001.0001.0001.0000.1010.0000.0131.0000.0001.0000.0190.0860.0000.0000.049
DOT_CODE0.9990.9990.999-0.079-0.053-0.006-0.0020.014-0.036-0.099-0.0420.1810.0130.042-0.1900.009-0.1010.0001.000-0.0200.3190.2130.2780.013-0.006
ELAPSED_TIME0.1760.1760.1760.9790.1170.0290.037-0.0460.974-0.008-0.1470.301-0.0290.0110.098-0.0490.9621.000-0.0201.000-0.3100.2160.197-0.0520.034
FL_NUMBER0.4550.4550.455-0.338-0.045-0.007-0.021-0.010-0.3110.0070.023-0.056-0.039-0.011-0.083-0.006-0.3550.0190.319-0.3101.000-0.0440.0900.002-0.015
TAXI_IN0.0700.0700.0700.1330.115-0.039-0.038-0.0570.171-0.122-0.1020.310-0.0430.009-0.059-0.0590.1180.0860.2130.216-0.0441.0000.077-0.058-0.045
TAXI_OUT0.1010.1010.1010.0630.2670.0400.0300.0220.107-0.093-0.1500.447-0.0340.0480.0270.0290.0480.0000.2780.1970.0900.0771.0000.0480.044
WHEELS_OFF0.0510.0510.051-0.0580.1700.7760.8100.956-0.063-0.0480.241-0.0410.0130.0100.1930.985-0.0530.0000.013-0.0520.002-0.0580.0481.0000.796
WHEELS_ON0.0500.0500.0500.0310.1170.9770.9110.7590.026-0.0720.1290.0170.0070.0170.1390.7820.0380.049-0.0060.034-0.015-0.0450.0440.7961.000

Missing values

2025-07-17T06:02:51.606632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-17T06:02:51.724788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-07-17T06:02:51.891621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

FL_DATEAIRLINEAIRLINE_DOTAIRLINE_CODEDOT_CODEFL_NUMBERORIGINORIGIN_CITYDESTDEST_CITYCRS_DEP_TIMEDEP_TIMEDEP_DELAYTAXI_OUTWHEELS_OFFWHEELS_ONTAXI_INCRS_ARR_TIMEARR_TIMEARR_DELAYCANCELLEDCANCELLATION_CODEDIVERTEDCRS_ELAPSED_TIMEELAPSED_TIMEAIR_TIMEDISTANCEDELAY_DUE_CARRIERDELAY_DUE_WEATHERDELAY_DUE_NASDELAY_DUE_SECURITYDELAY_DUE_LATE_AIRCRAFT
02022-07-19SkyWest Airlines Inc.SkyWest Airlines Inc.: OOOO203043371SANSan Diego, CASFOSan Francisco, CA17051700.0-5.013.01713.01816.011.018341827.0-7.00.0NaN0.089.087.063.0447.0NaNNaNNaNNaNNaN
12022-09-13Republic AirlineRepublic Airline: YXYX204523552CMHColumbus, OHORDChicago, IL11191118.0-1.016.01134.01125.011.011471136.0-11.00.0NaN0.088.078.051.0296.0NaNNaNNaNNaNNaN
22022-07-09SkyWest Airlines Inc.SkyWest Airlines Inc.: OOOO203044660CVGCincinnati, OHORDChicago, IL11181113.0-5.018.01131.01124.08.011551132.0-23.00.0NaN0.097.079.053.0264.0NaNNaNNaNNaNNaN
32021-03-19United Air Lines Inc.United Air Lines Inc.: UAUA19977325DENDenver, COMCIKansas City, MO18151815.00.015.01830.02042.05.020512047.0-4.00.0NaN0.096.092.072.0533.0NaNNaNNaNNaNNaN
42020-01-03United Air Lines Inc.United Air Lines Inc.: UAUA199771561IAHHouston, TXSFOSan Francisco, CA10001001.01.021.01022.01205.09.012271214.0-13.00.0NaN0.0267.0253.0223.01635.0NaNNaNNaNNaNNaN
52023-06-18SkyWest Airlines Inc.SkyWest Airlines Inc.: OOOO203043703ATWAppleton, WIDTWDetroit, MI16591650.0-9.019.01709.01856.014.019261910.0-16.00.0NaN0.087.080.047.0296.0NaNNaNNaNNaNNaN
62023-05-20SkyWest Airlines Inc.SkyWest Airlines Inc.: OOOO203044874ABQAlbuquerque, NMLAXLos Angeles, CA13341330.0-4.012.01342.01429.05.014451434.0-11.00.0NaN0.0131.0124.0107.0677.0NaNNaNNaNNaNNaN
72019-09-23PSA Airlines Inc.PSA Airlines Inc.: OHOH203975644CRWCharleston/Dunbar, WVCLTCharlotte, NC902858.0-4.022.0920.01002.016.010301018.0-12.00.0NaN0.088.080.042.0221.0NaNNaNNaNNaNNaN
82022-04-23Endeavor Air Inc.Endeavor Air Inc.: 9E9E203634809TRIBristol/Johnson City/Kingsport, TNATLAtlanta, GA600558.0-2.016.0614.0654.04.0705658.0-7.00.0NaN0.065.060.040.0227.0NaNNaNNaNNaNNaN
92019-11-22Spirit Air LinesSpirit Air Lines: NKNK20416612LASLas Vegas, NVMSPMinneapolis, MN126122.0-4.010.0132.0620.04.0625624.0-1.00.0NaN0.0179.0182.0168.01299.0NaNNaNNaNNaNNaN
FL_DATEAIRLINEAIRLINE_DOTAIRLINE_CODEDOT_CODEFL_NUMBERORIGINORIGIN_CITYDESTDEST_CITYCRS_DEP_TIMEDEP_TIMEDEP_DELAYTAXI_OUTWHEELS_OFFWHEELS_ONTAXI_INCRS_ARR_TIMEARR_TIMEARR_DELAYCANCELLEDCANCELLATION_CODEDIVERTEDCRS_ELAPSED_TIMEELAPSED_TIMEAIR_TIMEDISTANCEDELAY_DUE_CARRIERDELAY_DUE_WEATHERDELAY_DUE_NASDELAY_DUE_SECURITYDELAY_DUE_LATE_AIRCRAFT
99902023-02-23Southwest Airlines Co.Southwest Airlines Co.: WNWN193932437ONTOntario, CAOAKOakland, CA810810.00.06.0816.0920.03.0930923.0-7.00.0NaN0.080.073.064.0362.0NaNNaNNaNNaNNaN
99912021-09-22United Air Lines Inc.United Air Lines Inc.: UAUA19977252HNLHonolulu, HIIAHHouston, TX19451937.0-8.022.01959.0808.03.0814811.0-3.00.0NaN0.0449.0454.0429.03904.0NaNNaNNaNNaNNaN
99922023-04-10Southwest Airlines Co.Southwest Airlines Co.: WNWN193931580RICRichmond, VAATLAtlanta, GA17201716.0-4.014.01730.01842.06.019051848.0-17.00.0NaN0.0105.092.072.0481.0NaNNaNNaNNaNNaN
99932019-07-20Alaska Airlines Inc.Alaska Airlines Inc.: ASAS19930708SFOSan Francisco, CARDURaleigh/Durham, NC21202119.0-1.015.02134.0534.05.0535539.04.00.0NaN0.0315.0320.0300.02400.0NaNNaNNaNNaNNaN
99942022-04-12Republic AirlineRepublic Airline: YXYX204525700BOSBoston, MACHSCharleston, SC12101228.018.016.01244.01447.010.014491457.08.00.0NaN0.0159.0149.0123.0818.0NaNNaNNaNNaNNaN
99952022-06-21Envoy AirEnvoy Air: MQMQ203983738DSMDes Moines, IADFWDallas/Fort Worth, TX11211110.0-11.010.01120.01247.08.013221255.0-27.00.0NaN0.0121.0105.087.0624.0NaNNaNNaNNaNNaN
99962023-06-23United Air Lines Inc.United Air Lines Inc.: UAUA199772676ORDChicago, ILPDXPortland, OR20032131.088.030.02201.02358.04.022262.096.00.0NaN0.0263.0271.0237.01739.026.00.08.00.062.0
99972022-02-07PSA Airlines Inc.PSA Airlines Inc.: OHOH203975058PHLPhiladelphia, PAGSPGreer, SC18162047.0151.017.02104.02231.03.020092234.0145.00.0NaN0.0113.0107.087.0515.00.00.00.00.0145.0
99982020-03-12JetBlue AirwaysJetBlue Airways: B6B6204091286PITPittsburgh, PABOSBoston, MA838826.0-12.010.0836.0947.05.01012952.0-20.00.0NaN0.094.086.071.0496.0NaNNaNNaNNaNNaN
99992021-03-14Endeavor Air Inc.Endeavor Air Inc.: 9E9E203635111ABEAllentown/Bethlehem/Easton, PAATLAtlanta, GA640705.025.012.0717.0902.05.0903907.04.00.0NaN0.0143.0122.0105.0692.0NaNNaNNaNNaNNaN